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General Introduction to *ecositer*

Source: vignettes/intro.Rmd
intro.Rmd

This vignette is intended to document basic functionality of the ecositer package. This package is in development and should not be expected to perform in a robust manner across different datasets. At this point, the functionality here is a demonstration of workflows for dealing with NRCS ecological site data. I hope that future development of this package and standardization of NASIS population will make it more applicable.

Library

Begin by querying NASIS so that you have a selected set containing vegetation data. In this vignette, we will query for vegetation data in Sequoia and Kings Canyon National Parks, soil survey area CA792. Detailed instructions for making this query can be found here.

Now that we have data in our selected set, we will introduce the first ecositer function. vegStaticNASIS() takes a snapshot of your NASIS selected set and saves it in a local SQLite database. It is referred to as “static” because it does not change. This means that regardless of what is in your selected set in the future, you can refer to this static NASIS database using a local file path on your harddrive. Rather than switching between different queries and clearing your selected set in NASIS, you will likely find it useful to save a few specific selected sets locally on your computer. The functionality of creating static NASIS selected sets is borrowed from soilDB. vegStaticNASIS() is a special case designed specifically for vegetation data.

The only argument in this function is output_path the local file path where you want to save your static NASIS database.

ecositer::vegStaticNASIS(output_path = "C:/Users/Nathan.Roe/Documents/SEKI/CA792_veg.sqlite")

Let’s start off by looking at how many pedons and vegplots we have for each ecosite

numb_plots <- number_plots_by_site(static_location = "C:/Users/Nathan.Roe/Documents/SEKI/CA792_veg.sqlite")
knitr::kable(numb_plots)
ecositeid ecosite_simple pedons vegplots numb_statephases statephases
NA NA 163 1078 1 NA
F022AD102CA D102 78 35 4 D102.1.1, D102.1.1P, D102.1.2F, D102.1.3F
F022AH102CA H102 61 26 4 H102.1.4F, H102.1.1, H102.1.2F, H102.1.2A
F022AD104CA D104 50 33 2 D104.1.1, D104.1.4F
F022AH106CA H106 39 25 2 H106.1.1, H106.1.3F
F022AH101CA H101 38 19 4 H101.1.2F, H101.1.1, H101.1.3F, H101.1.4F
F022AB100CA B100 33 32 3 B100.1.1, B100.1.2F, B100.1.3F
F022AB114CA B114 33 27 2 B114.1.1, B114.1.3A
R022AB006CA B006 33 31 3 B006.1.1, B006.1.3E, B006.1.3A
R022AD002CA D002 33 13 3 D002.1.1, D002.1.3F, D002.1.2F
F022AB113CA B113 31 24 4 B113.1.1, B113.1.2F, B113.1.3A, B113.1.1P
F022AB111CA B111 30 33 4 B111.1.4A, B111.1.1, B111.1.2F, B111.1.3F
R022AD007CA D007 28 17 3 D007.1.3F, D007.1.2F, D007.1.1
R022AX008CA X008 28 23 1 X008.1.1
R022AX011CA X011 27 9 5 X011.1.2G, X011.1.1, X011.1.3G, X011.1.3E, X011.1.4G
R022AA102CA A102 26 19 2 A102.1.1, A102.1.1M
F022AH104CA H104 25 22 2 H104.1.3F, H104.1.1
R022AB001CA B001 25 17 1 B001.1.1
R022AD003CA D003 25 19 4 D003.1.1, D003.1.3A, D003.1.3F, D003.1.2F
F022AD103CA D103 24 25 4 D103.1.4F, D103.1.1, D103.1.2F, D103.1.3F
R022AH000CA H000 24 16 4 H000.1.2A, H000.1.1, H000.1.3F, H000.1.1P
R022AA106CA A106 23 17 1 A106.1.1
F022AK100CA K100 22 13 3 K100.1.1, K100.1.4G, K100.1.2F
F018XC201CA C201 21 13 3 C201.1.3G, C201.1.2F, C201.1.1
R022AK002CA K002 21 16 5 K002.1.3F, K002.1.1P, K002.1.1, K002.1.2F, K002.1.4F
R022AD001CA D001 20 21 4 D001.1.4A, D001.1.3A, D001.1.1, D001.1.2A
F018XC203CA C203 19 8 3 C203.1.1, C203.1.3G, C203.1.4G
R018XC107CA C107 19 4 2 C107.1.1, C107.1
F022AD100CA D100 17 17 4 D100.1.2F, D100.1.1, D100.1.3F, D100.1.4F
R022AA010CA A010 16 7 2 A010.1.2A, A010.1.1
R022AD009CA D009 16 10 2 D009.1.1, D009.1.1P
F022AX017CA X017 15 10 3 X017.1.1, X017.1.2U, X017.1.3U
R022AH001CA H001 15 9 3 H001.1.1, H001.1.2F, H001.1.3F
F022AX005CA X005 14 12 3 X005.1.1, X005.1.2F, X005.1.3A
R022AB010CA B010 13 8 3 B010.1.1, B010.1.4A, B010.1.2A
R022AX000CA X000 13 19 4 X000.1.3G, X000.1.4G, X000.1.1, X000.1.2G
F022AB108CA B108 12 7 2 B108.1.2F, B108.1.1
R022AA107CA A107 12 5 1 A107.1.1
R022AX006CA X006 12 16 3 X006.1.1, X006.1.1P, X006.1.1D
R022AX009CA X009 12 8 2 X009.1.3G, X009.1.1
F022AD101CA D101 11 7 3 D101.1.1, D101.1.4F, D101.1.3F
F022AX013CA X013 11 9 2 X013.1.1, X013.1.3A
R022AD010CA D010 11 1 1 D010.1.2A
F022AH203CA H203 10 9 3 H203.1.1, H203.1.2F, H203.1.3F
R022AX010CA X010 10 9 2 X010.1.1P, X010.1.1
F022AK101CA K101 9 8 2 K101.1.2F, K101.1.1
R022AK001CA K001 9 6 3 K001.1.2F, K001.1.3F, K001.1.1
R022AX007CA X007 9 16 4 X007.1.1, X007.1.3G, X007.1.1D, X007.1.4E
F018XC110CA C110 8 NA NA NA
F022AD105CA D105 8 10 5 D105.1.4F, D105.1.3F, D105.1.1, D105.1.1P, D105.1.2F
F022AK104CA K104 8 6 4 K104.1.1, K104.1.4G, K104.1.4B, K104.1.3F
F022AX014CA X014 8 4 1 X014.1.1
R022AA101CA A101 8 8 1 A101.1.1
R022AA104CA A104 7 NA NA NA
R022AB009CA B009 7 5 2 B009.1.1, B009.1.4A
F022AX003CA X003 6 8 1 X003.1.1
R022AB004CA B004 6 4 3 B004.1.2F, B004.1.1, B004.1.3A
R022AX002CA X002 6 7 3 X002.1.1, X002.1.3E, X002.1.2A
R018XX101CA X101 5 NA NA NA
R022AX001CA X001 5 9 3 X001.1.1P, X001.1.1, X001.1.1D
F018XC109CA C109 4 4 1 C109.1.1
F022AH201CA H201 4 5 3 H201.1.1, H201.1.2F, H201.1.3F
F022AH202CA H202 4 5 2 H202.1.3F, H202.1.2F
R022AB002CA B002 3 2 1 B002.1.1
R022AB012CA B012 3 2 1 B012.1.1
R022AX004CA X004 2 6 1 X004.1.1
R018XX100CA X100 1 NA NA NA
R022AD004CA D004 1 4 1 D004.1.1
R022AD005CA D005 1 NA NA NA
NA X020 NA 11 3 X020.1.1, X020.1.2G, X020.1.4A
NA X016 NA 6 3 X016.1.1M, X016.1.2F, X016.1.1
NA X199 NA 6 1 X199.1.1
NA 1804 NA 3 2 1804.1.1, 1804.1.3F
NA X999 NA 2 1 X999.1.1
NA B007 NA 1 1 B007.1.1P
NA B008 NA 1 1 B008.1.1

Now we can format our vegetation data into a more useful format

CA792_veg_formatted <- formatted_veg_df(static_location = "C:/Users/Nathan.Roe/Documents/SEKI/CA792_veg.sqlite")

Here is a look at a portion of the data we have assembled.

DT::datatable(CA792_veg_formatted |> dplyr::select(-primarydatacollector, - plantnatvernm, -akfieldecositeid,
                                                   -vegplotid, -vegplotiid, -plantsym))

Now that the data is formatted we can begin to analyze it.

CA792_veg_summary <- ecositer::veg_summary(veg_df = CA792_veg_formatted)

The result of veg_summary() is a list. This list provides a vegetation summary for every ecosite and state/phase in your dataset, as well as raw dataset by ecosite and state/phase.

listviewer::jsonedit(CA792_veg_summary, width = 750)
CA792_veg_summary$R022AB006CA$STM$B006.1.1 |> View()

Now let’s look at a vegetation summary, including elements like importance, Indicator Species Analysis, and more.

CA792_veg_summary$R022AB006CA$Cover_data |> knitr::kable()  |>  kable_styling("striped", 
                  full_width = TRUE) |> kableExtra::scroll_box(width = "750px", height = "500px")
constancy avg_abund twenty_percentile eighty_percentile median_abund max_abund min_abund sum_abund numb_plots_found numb_plots_not_found perc_obs_pres_abs perc_obs_in_ecosite perc_abund_in_ecosite importance ISA_p.value
Juncus parryi 83.870968 2.9774194 0.1 6.0 2.00 12.0 0 92.3 26 5 0 0.1125541 0.1283906 0.0144509 NA
Pinus albicaulis 70.967742 14.5419355 0.0 25.0 7.00 85.0 0 450.8 22 9 0 0.1788618 0.3384384 0.0605337 NA
Carex rossii 64.516129 0.5870968 0.0 1.0 0.75 3.0 0 18.2 20 11 0 0.1156069 0.0652096 0.0075387 NA
Carex filifolia 64.516129 10.3258065 0.0 22.0 13.00 50.0 0 320.1 20 11 0 0.1257862 0.1414682 0.0177947 NA
Elymus elymoides 58.064516 0.6451613 0.0 1.0 0.50 5.0 0 20.0 18 13 0 0.0983607 0.0888099 0.0087354 NA
Poa secunda 51.612903 0.4451613 0.0 1.0 0.75 2.0 0 13.8 16 15 0 0.1797753 0.1188630 0.0213686 NA
Pinus contorta var. murrayana 41.935484 1.6322581 0.0 2.5 2.50 19.0 0 50.6 13 18 0 0.0445205 0.0108731 0.0004841 NA
Selaginella watsonii 38.709677 0.7032258 0.0 2.0 2.00 4.0 0 21.8 12 19 0 0.1008403 0.0753283 0.0075961 NA
Penstemon newberryi 35.483871 1.1000000 0.0 2.0 2.00 12.0 0 34.1 11 20 0 0.1057692 0.2099754 0.0222089 NA
Cistanthe umbellata var. umbellata 32.258065 0.1516129 0.0 0.5 0.50 1.0 0 4.7 10 21 0 0.2127660 0.2701149 0.0574713 0.4
Eriogonum ovalifolium 22.580645 0.0774194 0.0 0.1 0.10 1.0 0 2.4 7 24 0 0.1944444 0.0933852 0.0181582 NA
Trisetum spicatum 22.580645 0.1838710 0.0 0.1 0.50 2.0 0 5.7 7 24 0 0.0744681 0.0553398 0.0041210 NA
Eriogonum nudum 22.580645 0.1322581 0.0 0.1 0.50 1.0 0 4.1 7 24 0 0.0777778 0.0686767 0.0053415 NA
Eriogonum rosense 22.580645 0.2580645 0.0 0.5 1.00 2.0 0 8.0 7 24 0 0.1555556 0.1199400 0.0186573 NA
Achnatherum occidentale 22.580645 0.2000000 0.0 0.1 0.50 3.0 0 6.2 7 24 0 0.0598291 0.0185462 0.0011096 NA
Antennaria rosea 19.354839 0.2967742 0.0 0.0 0.50 7.0 0 9.2 6 25 0 0.0714286 0.0676471 0.0048319 NA
Antennaria 19.354839 0.0870968 0.0 0.0 0.50 1.0 0 2.7 6 25 0 0.0740741 0.0147300 0.0010911 NA
Penstemon 19.354839 0.1967742 0.0 0.0 0.75 2.0 0 6.1 6 25 0 0.0937500 0.0729665 0.0068406 NA
Arabis 19.354839 0.0967742 0.0 0.0 0.50 0.5 0 3.0 6 25 0 0.0571429 0.0785340 0.0044877 NA
Linanthus pungens 19.354839 0.0580645 0.0 0.0 0.30 0.5 0 1.8 6 25 0 0.1428571 0.0687023 0.0098146 NA
Penstemon rydbergii 19.354839 0.2258065 0.0 0.0 1.00 2.0 0 7.0 6 25 0 0.1071429 0.0612423 0.0065617 NA
Carex 16.129032 0.0838710 0.0 0.0 0.50 1.0 0 2.6 5 26 0 0.0176678 0.0014559 0.0000257 NA
Potentilla drummondii 16.129032 0.0677419 0.0 0.0 0.50 0.5 0 2.1 5 26 0 0.1351351 0.0419162 0.0056643 NA
Poa 16.129032 0.1161290 0.0 0.0 0.50 2.0 0 3.6 5 26 0 0.0438596 0.0196399 0.0008614 NA
Phyllodoce breweri 12.903226 0.1935484 0.0 0.0 1.50 2.0 0 6.0 4 27 0 0.0384615 0.0066196 0.0002546 NA
Eriogonum incanum 12.903226 0.1612903 0.0 0.0 1.00 2.0 0 5.0 4 27 0 0.1333333 0.2732240 0.0364299 NA
Solidago multiradiata 12.903226 0.0387097 0.0 0.0 0.30 0.5 0 1.2 4 27 0 0.0579710 0.0076923 0.0004459 NA
Ivesia 12.903226 0.0967742 0.0 0.0 0.75 1.0 0 3.0 4 27 0 0.1481481 0.0471698 0.0069881 NA
Achnatherum 12.903226 0.0645161 0.0 0.0 0.50 0.5 0 2.0 4 27 0 0.0430108 0.0139958 0.0006020 NA
Arabis platysperma 9.677419 0.0387097 0.0 0.0 0.10 1.0 0 1.2 3 28 0 0.0857143 0.0909091 0.0077922 NA
Penstemon heterodoxus 9.677419 0.1000000 0.0 0.0 1.00 2.0 0 3.1 3 28 0 0.0857143 0.0780856 0.0066931 NA
Poa stebbinsii 9.677419 0.0387097 0.0 0.0 0.10 1.0 0 1.2 3 28 0 0.4285714 0.1621622 0.0694981 NA
Ribes montigenum 9.677419 0.0387097 0.0 0.0 0.10 1.0 0 1.2 3 28 0 0.0285714 0.0055607 0.0001589 NA
Monardella odoratissima 9.677419 0.0645161 0.0 0.0 0.50 1.0 0 2.0 3 28 0 0.0428571 0.0208986 0.0008957 NA
Achillea millefolium 9.677419 0.0645161 0.0 0.0 0.50 1.0 0 2.0 3 28 0 0.0329670 0.0253485 0.0008357 NA
Holodiscus discolor 9.677419 0.0225806 0.0 0.0 0.10 0.5 0 0.7 3 28 0 0.0447761 0.0027058 0.0001212 NA
Pinus 9.677419 0.4548387 0.0 0.0 5.10 8.5 0 14.1 3 28 0 0.2142857 0.0891841 0.0191109 NA
Eriogonum 9.677419 0.1774194 0.0 0.0 2.00 3.0 0 5.5 3 28 0 0.0769231 0.0418569 0.0032198 NA
Rumex 9.677419 0.0483871 0.0 0.0 0.50 0.5 0 1.5 3 28 0 0.1034483 0.1209677 0.0125139 NA
Poa cusickii 9.677419 0.0838710 0.0 0.0 0.50 2.0 0 2.6 3 28 0 0.0612245 0.0295791 0.0018110 NA
Koeleria macrantha 9.677419 0.1322581 0.0 0.0 2.00 2.0 0 4.1 3 28 0 0.0731707 0.0424870 0.0031088 NA
Pinus balfouriana 9.677419 0.3290323 0.0 0.0 5.00 5.1 0 10.2 3 28 0 0.0394737 0.0147955 0.0005840 NA
Aster 6.451613 0.0193548 0.0 0.0 0.30 0.5 0 0.6 2 29 0 0.0270270 0.0034325 0.0000928 NA
Gentianopsis simplex 6.451613 0.0064516 0.0 0.0 0.10 0.1 0 0.2 2 29 0 0.2500000 0.0112360 0.0028090 NA
Penstemon davidsonii 6.451613 0.0064516 0.0 0.0 0.10 0.1 0 0.2 2 29 0 0.1052632 0.0117647 0.0012384 NA
Raillardella scaposa 6.451613 0.0193548 0.0 0.0 0.30 0.5 0 0.6 2 29 0 0.2222222 0.1224490 0.0272109 NA
Salix orestera 6.451613 0.1967742 0.0 0.0 3.05 6.0 0 6.1 2 29 0 0.0298507 0.0077147 0.0002303 NA
Cryptogramma acrostichoides 6.451613 0.0193548 0.0 0.0 0.30 0.5 0 0.6 2 29 0 0.0555556 0.0491803 0.0027322 NA
Sibbaldia procumbens 6.451613 0.0193548 0.0 0.0 0.30 0.5 0 0.6 2 29 0 0.0454545 0.0107720 0.0004896 NA
Poa wheeleri 6.451613 0.1935484 0.0 0.0 3.00 5.0 0 6.0 2 29 0 0.0338983 0.0256082 0.0008681 NA
Heuchera rubescens 6.451613 0.0193548 0.0 0.0 0.30 0.5 0 0.6 2 29 0 0.0909091 0.0137931 0.0012539 NA
Senecio 6.451613 0.0193548 0.0 0.0 0.30 0.5 0 0.6 2 29 0 0.0357143 0.0043259 0.0001545 NA
Silene 6.451613 0.0193548 0.0 0.0 0.30 0.5 0 0.6 2 29 0 0.0909091 0.0487805 0.0044346 NA
Silene lemmonii 6.451613 0.0322581 0.0 0.0 0.50 0.5 0 1.0 2 29 0 0.3333333 0.7142857 0.2380952 NA
Vaccinium cespitosum 6.451613 0.2096774 0.0 0.0 3.25 6.0 0 6.5 2 29 0 0.0238095 0.0078465 0.0001868 NA
Agrostis 6.451613 0.0193548 0.0 0.0 0.30 0.5 0 0.6 2 29 0 0.0588235 0.0026810 0.0001577 NA
Muhlenbergia richardsonis 6.451613 0.0193548 0.0 0.0 0.30 0.5 0 0.6 2 29 0 0.0800000 0.0346821 0.0027746 NA
Agrostis idahoensis 6.451613 0.0322581 0.0 0.0 0.50 0.5 0 1.0 2 29 0 0.0416667 0.0027093 0.0001129 NA
Antennaria corymbosa 6.451613 0.0645161 0.0 0.0 1.00 1.0 0 2.0 2 29 0 0.0666667 0.0584795 0.0038986 NA
Castilleja 6.451613 0.0193548 0.0 0.0 0.30 0.5 0 0.6 2 29 0 0.0350877 0.0155844 0.0005468 NA
Erigeron 6.451613 0.0322581 0.0 0.0 0.50 0.5 0 1.0 2 29 0 0.0555556 0.0227790 0.0012655 NA
Arenaria kingii 6.451613 0.0677419 0.0 0.0 1.05 2.0 0 2.1 2 29 0 0.2857143 0.4468085 0.1276596 NA
Potentilla 6.451613 0.0354839 0.0 0.0 0.55 1.0 0 1.1 2 29 0 0.0289855 0.0133011 0.0003855 NA
Symphoricarpos rotundifolius 6.451613 0.0354839 0.0 0.0 0.55 1.0 0 1.1 2 29 0 0.0370370 0.0069975 0.0002592 NA
Danthonia unispicata 3.225807 0.0032258 0.0 0.0 0.10 0.1 0 0.1 1 30 0 0.1666667 0.0153846 0.0025641 NA
Luzula subcongesta 3.225807 0.0032258 0.0 0.0 0.10 0.1 0 0.1 1 30 0 0.0666667 0.0042553 0.0002837 NA
Danthonia 3.225807 0.0322581 0.0 0.0 1.00 1.0 0 1.0 1 30 0 0.1000000 0.0578035 0.0057803 NA
Achnatherum latiglume 3.225807 0.0322581 0.0 0.0 1.00 1.0 0 1.0 1 30 0 0.1666667 0.2325581 0.0387597 NA
Castilleja applegatei 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0232558 0.0165563 0.0003850 NA
Ribes 3.225807 0.0967742 0.0 0.0 3.00 3.0 0 3.0 1 30 0 0.0119048 0.0157978 0.0001881 NA
Salix 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0147059 0.0007350 0.0000108 NA
Achnatherum pinetorum 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.5000000 0.8333333 0.4166667 NA
Ericameria 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.3333333 0.7142857 0.2380952 NA
Poa fendleriana 3.225807 0.0322581 0.0 0.0 1.00 1.0 0 1.0 1 30 0 0.1250000 0.0709220 0.0088652 NA
Rhodiola integrifolia ssp. integrifolia 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0666667 0.0833333 0.0055556 NA
Ribes cereum 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0144928 0.0038521 0.0000558 NA
Rumex acetosella 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.1111111 0.1515152 0.0168350 NA
Luzula 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0476190 0.0222222 0.0010582 NA
Ivesia santolinoides 3.225807 0.3225806 0.0 0.0 10.00 10.0 0 10.0 1 30 0 0.1000000 0.3378378 0.0337838 NA
Lupinus albicaulis 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0769231 0.0274725 0.0021133 NA
Festuca 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0666667 0.1136364 0.0075758 NA
Microseris 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.1428571 0.1136364 0.0162338 NA
Phlox diffusa 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0769231 0.0373134 0.0028703 NA
Sphenosciadium capitellatum 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0277778 0.0081169 0.0002255 NA
Antennaria pulchella 3.225807 0.0322581 0.0 0.0 1.00 1.0 0 1.0 1 30 0 0.1111111 0.0319489 0.0035499 NA
Muhlenbergia filiformis 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0357143 0.0044683 0.0001596 NA
Oreostemma peirsonii 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0909091 0.0284091 0.0025826 NA
Potentilla glandulosa 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0400000 0.0322581 0.0012903 NA
Sedum 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0500000 0.0122249 0.0006112 NA
Elymus trachycaulus ssp. trachycaulus 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0322581 0.0051867 0.0001673 NA
Juncus orthophyllus 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0909091 0.0116550 0.0010595 NA
Oreostemma alpigenum var. andersonii 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0109890 0.0006044 0.0000066 NA
Pedicularis attollens 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0476190 0.0357143 0.0017007 NA
Eragrostis intermedia 3.225807 0.0322581 0.0 0.0 1.00 1.0 0 1.0 1 30 0 0.2500000 0.0892857 0.0223214 NA
Hieracium horridum 3.225807 0.0161290 0.0 0.0 0.50 0.5 0 0.5 1 30 0 0.0166667 0.0251256 0.0004188 NA
Calochortus 3.225807 0.0322581 0.0 0.0 1.00 1.0 0 1.0 1 30 0 0.0714286 0.3846154 0.0274725 NA
Raillardella argentea 3.225807 0.0645161 0.0 0.0 2.00 2.0 0 2.0 1 30 0 0.3333333 0.9090909 0.3030303 0.9
Selaginella 3.225807 0.0322581 0.0 0.0 1.00 1.0 0 1.0 1 30 0 0.3333333 0.8333333 0.2777778 0.9
Cirsium 3.225807 0.0032258 0.0 0.0 0.10 0.1 0 0.1 1 30 0 0.0434783 0.0031949 0.0001389 NA
Oreonana 3.225807 0.0032258 0.0 0.0 0.10 0.1 0 0.1 1 30 0 1.0000000 1.0000000 1.0000000 0.9
Ericameria discoidea 3.225807 0.0322581 0.0 0.0 1.00 1.0 0 1.0 1 30 0 0.1428571 0.5000000 0.0714286 NA
Artemisia tridentata ssp. vaseyana 3.225807 0.0032258 0.0 0.0 0.10 0.1 0 0.1 1 30 0 0.0196078 0.0003382 0.0000066 NA
Lonicera 3.225807 0.0032258 0.0 0.0 0.10 0.1 0 0.1 1 30 0 0.0384615 0.0052910 0.0002035 NA
Phlox 3.225807 0.0032258 0.0 0.0 0.10 0.1 0 0.1 1 30 0 0.0294118 0.0020284 0.0000597 NA
Lupinus lepidus 3.225807 0.0032258 0.0 0.0 0.10 0.1 0 0.1 1 30 0 0.0250000 0.0009116 0.0000228 NA
Calamagrostis breweri 3.225807 0.0645161 0.0 0.0 2.00 2.0 0 2.0 1 30 0 0.0136986 0.0018260 0.0000250 NA

One useful tool for looking at ecological data is Non-Metric Multidimensional Scaling.

my_nmds <- ecositer::nmds_ecosite(veg_summary = CA792_veg_summary, ecosite = c("F022AK100CA", "F022AK101CA"), pres_abs = TRUE, nmds_dim = 2, reduce_species = NA)
#> Run 0 stress 0.1920348 
#> Run 1 stress 0.1943657 
#> Run 2 stress 0.1943972 
#> Run 3 stress 0.2243439 
#> Run 4 stress 0.1920353 
#> ... Procrustes: rmse 0.0003189586  max resid 0.001551779 
#> ... Similar to previous best
#> Run 5 stress 0.2252217 
#> Run 6 stress 0.2182233 
#> Run 7 stress 0.194454 
#> Run 8 stress 0.2016835 
#> Run 9 stress 0.1946112 
#> Run 10 stress 0.195011 
#> Run 11 stress 0.2183573 
#> Run 12 stress 0.1994242 
#> Run 13 stress 0.1948882 
#> Run 14 stress 0.1922434 
#> ... Procrustes: rmse 0.01143298  max resid 0.03565544 
#> Run 15 stress 0.1920421 
#> ... Procrustes: rmse 0.007718833  max resid 0.03140165 
#> Run 16 stress 0.2039612 
#> Run 17 stress 0.1998307 
#> Run 18 stress 0.2279321 
#> Run 19 stress 0.1994438 
#> Run 20 stress 0.2219273 
#> Run 21 stress 0.2018171 
#> Run 22 stress 0.1923712 
#> ... Procrustes: rmse 0.02039892  max resid 0.09053014 
#> Run 23 stress 0.2275599 
#> Run 24 stress 0.2242465 
#> Run 25 stress 0.194366 
#> Run 26 stress 0.194591 
#> Run 27 stress 0.2206002 
#> Run 28 stress 0.1943978 
#> Run 29 stress 0.2161539 
#> Run 30 stress 0.1952732 
#> Run 31 stress 0.1944228 
#> Run 32 stress 0.1920359 
#> ... Procrustes: rmse 0.0005662958  max resid 0.002767675 
#> ... Similar to previous best
#> Run 33 stress 0.2224874 
#> Run 34 stress 0.194675 
#> Run 35 stress 0.1944978 
#> Run 36 stress 0.2284606 
#> Run 37 stress 0.2068999 
#> Run 38 stress 0.1920438 
#> ... Procrustes: rmse 0.007926858  max resid 0.03128936 
#> Run 39 stress 0.2069103 
#> Run 40 stress 0.1963972 
#> Run 41 stress 0.2047695 
#> Run 42 stress 0.2364156 
#> Run 43 stress 0.2155448 
#> Run 44 stress 0.204869 
#> Run 45 stress 0.1944059 
#> Run 46 stress 0.220569 
#> Run 47 stress 0.2252724 
#> Run 48 stress 0.1920611 
#> ... Procrustes: rmse 0.008930358  max resid 0.03074702 
#> Run 49 stress 0.1944069 
#> Run 50 stress 0.1920417 
#> ... Procrustes: rmse 0.002178437  max resid 0.01072892 
#> *** Best solution repeated 2 times

Visualize NMDS

nmds_plot(static_location = "C:/Users/Nathan.Roe/Documents/SEKI/CA792_veg.sqlite", 
          nmds = my_nmds,
          veg_summary = CA792_veg_summary)